Kokoro-82M — GGUF (ggml-quantised)
GGUF / ggml conversion of hexgrad/Kokoro-82M for use with CrispStrobe/CrispASR.
Kokoro-82M is an open-weight 82 M-parameter TTS model built on the StyleTTS2 architecture. It is multilingual at the model level (178-symbol IPA vocab covering en, es, fr, hi, it, ja, pt, zh) and ships per-language voice packs in hexgrad/Kokoro-82M/voices. Apache-2.0 weights.
Files
| File | Quant | Size | Notes |
|---|---|---|---|
kokoro-82m-f16.gguf | F16 | 156 MB | Reference quality — bit-true to the PyTorch model on the deterministic stages |
kokoro-82m-q8_0.gguf | Q8_0 | 135 MB | Recommended — ASR roundtrip identical to F16 |
Q4_K is not published: per the crispasr-diff kokoro harness it falls below quality bar (cosine similarity collapses to ~0.1 on audio_out and the German backbone produces unintelligible output). Q8_0 saves ~13 % on disk over F16 with no observable quality loss.
Quick start
# 1. Build CrispASR
git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j --target crispasr
# 2. Pull the model + a voice pack
huggingface-cli download cstr/kokoro-82m-GGUF kokoro-82m-q8_0.gguf --local-dir .
huggingface-cli download cstr/kokoro-voices-GGUF kokoro-voice-af_heart.gguf --local-dir .
# 3. Synthesise
./build/bin/crispasr --backend kokoro \
-m kokoro-82m-q8_0.gguf \
--voice kokoro-voice-af_heart.gguf \
--tts "Hello, this is a test of the English phonemizer." \
--tts-output hello.wav
24 kHz mono WAV. --language <code> switches the in-process libespeak-ng phonemizer (en, es, fr, it, pt, hi, ja, zh — and de/ru when paired with the German backbone).
Voice packs
Voices ship in the sister repo cstr/kokoro-voices-GGUF so the model and per-speaker style files can be versioned independently. CrispASR auto-routes by language when both files sit in the same directory.
Quality verification
ASR roundtrip via parakeet-tdt-0.6b-v3 -l en, voice af_heart:
| Quant | Synthesised text | Parakeet output |
|---|---|---|
| F16 | "Hello, this is a test of the English phonemizer." | "Hello this is a test of the English Phone Miza." |
| Q8_0 | (same text) | "Hello this is a test of the English Phone Miza." |
Both quants produce ASR-identical output. The trailing "Phone Miza" is parakeet mis-transcribing the rare word "phonemizer", not a TTS artefact.
crispasr-diff kokoro against the PyTorch reference:
| Stage (selection) | F16 cos_min | Q8_0 cos_min |
|---|---|---|
| token_ids → durations | 1.000 | 1.000 |
pred_lstm_out | 0.999 | 0.997 |
dec_decode_3_out | 0.9999 | 0.9990 |
audio_out (RNG-divergent) | 0.99 | 0.85 |
F16 hits 16/16 PASS at cos≥0.999. Q8_0 hits 9/16 PASS at the same threshold; the failed stages stay above cos≥0.85 and don't perturb ASR roundtrip.
Architecture
| Component | Details |
|---|---|
| Text encoder | 178-IPA-symbol embedding → 1× CNN block → bidirectional LSTM |
| pl-BERT | 12-layer transformer, 768 d, 12 heads (text prosody features) |
| Predictor | Duration LSTM + F0/N curves |
| Decoder (iSTFTNet) | 3-stage upsample (10×6 = 60×) + ResBlocks + iSTFT (n_fft=20, hop=5) |
| Style dim | 256 (split as 128+128 between predictor and decoder) |
| Audio | 24 kHz mono output; predictor consumes phoneme IDs |
Conversion
python models/convert-kokoro-to-gguf.py \
--input hexgrad/Kokoro-82M \
--output kokoro-82m-f16.gguf \
--outtype f16
build/bin/crispasr-quantize kokoro-82m-f16.gguf kokoro-82m-q8_0.gguf q8_0
The converter handles both legacy weight_g/weight_v and the modern parametrizations.weight.original{0,1} WeightNorm forms, so it also runs on community re-trains like the dida-80b German backbone.
Attribution
- Original model:
hexgrad/Kokoro-82M(Apache-2.0). hexgrad et al. - Architecture: StyleTTS2-LJSpeech by Yinghao Aaron Li.
- GGUF conversion + ggml runtime:
CrispStrobe/CrispASR— seesrc/kokoro.cppandmodels/convert-kokoro-to-gguf.py.
License
Apache-2.0, inherited from the base model.
Provenance and EU AI Act Art. 53 note
- Upstream model: hexgrad/Kokoro-82M — published by
hexgrad. - Upstream licence:
apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.